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Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

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Design Example

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The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...

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Linear Approximation in Time Domain

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Related Experiment Videos

Two algorithms for neural-network design and training with application to channel equalization.

C Z Sweatman¹, B Mulgrew, G J Gibson

¹Department of Electrical Engineering, University of Edinburgh, Edinburgh EH9 3JL, UK.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This summary is machine-generated.

Two novel algorithms, the linear programming slab algorithm (LPSA) and perceptron learning slab algorithm (PLSA), efficiently train neural-network classifiers for signal reconstruction and adaptive equalization, showing strong performance in simulations.

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Area of Science:

Machine Learning
Signal Processing
Telecommunications

Background:

Digital signal reconstruction is challenging due to channel dispersion and noise.
Adaptive equalization is crucial for reliable data transmission over complex channels.

Purpose of the Study:

To introduce two new algorithms for designing and training neural-network classifiers.
To apply these algorithms for adaptive equalization of a 4-quadrature amplitude modulation (QAM) channel.

Main Methods:

Developed the linear programming slab algorithm (LPSA) using linear programming for multilayer perceptron (MLP) parameter identification.
Developed the perceptron learning slab algorithm (PLSA) using an error-correction approach to reduce computational costs.
Exploited constrained parameter spaces and classification problem symmetry in both algorithms.

Main Results:

Both LPSA and PLSA demonstrated effectiveness in adaptive equalization procedures.
Simulations compared algorithm performance on stationary and time-varying channels (COST 207 GSM model).

Conclusions:

The developed algorithms offer efficient methods for neural-network classifier training.
These algorithms are suitable for adaptive equalization tasks in digital communication systems.